Abstract
The existing CCD-camera based systems for fall detection require time for installation and camera calibration. They do not preserve the privacy adequately and are unable to operate in low lighting conditions. In this paper we show how to achieve automatic fall detection using only depth images. The point cloud corresponding to floor is delineated automatically using v-disparity images and Hough transform. The ground plane is extracted by the RANSAC algorithm. The detection of the person takes place on the basis of the updated on-line depth reference images. Fall detection is achieved using a classifier trained on features representing the extracted person both in depth images and in point clouds. All fall events were recognized correctly on an image set consisting of 312 images of which 110 contained the human falls. The images were acquired by two Kinect sensors placed at two different locations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anderson, D., Keller, J., Skubic, M., Chen, X., He, Z.: Recognizing falls from silhouettes. In: Annual Int. Conf. of the Engineering in Medicine and Biology Society, pp. 6388–6391 (2006)
Bourke, A., O’Brien, J., Lyons, G.: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait & Posture 26(2), 194–199 (2007)
Cleary, J., Trigg, L.: An instance-based learner using an entropic distance measure. In: Int. Conf. on Machine Learning, pp. 108–114 (1995)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley (1992)
Cover, T.M., Thomas, J.A.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Cucchiara, R., Prati, A., Vezzani, R.: A multi-camera vision system for fall detection and alarm generation. Expert Systems 24(5), 334–345 (2007)
Degen, T., Jaeckel, H., Rufer, M., Wyss, S.: Speedy: A fall detector in a wrist watch. In: Proc. of IEEE Int. Symp. on Wearable Computers, pp. 184–187 (2003)
Jansen, B., Deklerck, R.: Context aware inactivity recognition for visual fall detection. In: Proc. IEEE Pervasive Health Conference and Workshops, pp. 1–4 (2006)
Kepski, M., Kwolek, B., Austvoll, I.: Fuzzy inference-based reliable fall detection using kinect and accelerometer. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 266–273. Springer, Heidelberg (2012)
Kepski, M., Kwolek, B.: Human fall detection using kinect sensor. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds.) CORES 2013. AISC, vol. 226, pp. 743–752. Springer, Heidelberg (2013)
Labayrade, R., Aubert, D., Tarel, J.P.: Real time obstacle detection in stereovision on non flat road geometry through “v-disparity” representation. In: IEEE Intelligent Vehicle Symposium, vol. 2, pp. 646–651 (2002)
Marshall, S.W., Runyan, C.W., Yang, J., Coyne-Beasley, T., Waller, A.E., Johnson, R.M., Perkis, D.: Prevalence of selected risk and protective factors for falls in the home. American J. of Preventive Medicine 8(1), 95–101 (2005)
Mastorakis, G., Makris, D.: Fall detection system using Kinect’s infrared sensor. J. of Real-Time Image Processing, 1–12 (2012)
Miaou, S.G., Sung, P.H., Huang, C.Y.: A customized human fall detection system using omni-camera images and personal information. Distributed Diagnosis and Home Healthcare, 39–42 (2006)
Mubashir, M., Shao, L., Seed, L.: A survey on fall detection: Principles and approaches. Neurocomputing 100, 144–152 (2013), special issue: Behaviours in video
Noury, N., Fleury, A., Rumeau, P., Bourke, A., ÓLaighin, G., Rialle, V., Lundy, J.: Fall detection - principles and methods. In: Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, pp. 1663–1666 (2007)
Tzeng, H.W., Chen, M.Y., Chen, J.Y.: Design of fall detection system with floor pressure and infrared image. In: Int. Conf. on System Science and Engineering, pp. 131–135 (2010)
Yu, X.: Approaches and principles of fall detection for elderly and patient. In: 10th Int. Conf. on e-Health Networking, Applications and Services, pp. 42–47 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kepski, M., Kwolek, B. (2013). Unobtrusive Fall Detection at Home Using Kinect Sensor. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_55
Download citation
DOI: https://doi.org/10.1007/978-3-642-40261-6_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40260-9
Online ISBN: 978-3-642-40261-6
eBook Packages: Computer ScienceComputer Science (R0)